Document Image Processing

1
Document Image Processing

• Geometrical Transforms
• Linear Filters
• Morphological Operations
• Connected Component Labeling
• Binarization
• Contour Tracing
• X-Y Cuts
• Smearing
• Fourier Transform
• Hough Transform
• Docstrum
• Moments and Features

2
Transform Invariants
Geometric Transforms
3
Affine Transforms
Affine transforms cover a linear combination of
translations, scale, and rotation I(x,y) is the
original image I(x,y) is the transformed image
rotation angle
slant angle
4
Linear Filters
Convolution Equation
Smoothing Low pass filter
Vertical Line Sensitive filter
Vertical edge Sensitive filter
Enhancement filter
Laplacian Edge Operator
5
Morphological Operators
6
Dilation

• For each background pixel superimpose the
  structuring element on top of the input image so
  that the origin of the structuring element
  coincides with the input pixel position.
• If at least one pixel in the structuring element
  coincides with a foreground pixel in the image
  underneath, then the input pixel is set to the
  foreground value.
• If all the corresponding pixels in the image are
  background, however, the input pixel is left at
  the background value.

7
Erosion

• For each foreground pixel superimpose the
  structuring element on top of the input image so
  that the origin of the structuring element
  coincides with the input pixel position.
• If every pixel in the structuring element
  coincides with a foreground pixel in the image
  underneath, then the input pixel is left as is.
• If any pixel coincides with background, however,
  the input pixel is changed to background.

8
Opening and Closing
Opening Erosion followed by Dilation using the
same kernel
Closing Dilation followed by Erosion using the
same kernel
9
Hit and Miss

• Kernel has 1s, 0s, and dont-care
• If the 1s and 0s in the kerenel exactly match 1s
  and 0s in image, then the pixel underneath the
  origin is set to 1 else 0
• Corner finding kernels
• Final result is OR of the outputs

• used to locate isolated points in a binary image.
  
• used to locate the end points on a binary
  skeleton -four hit-and-miss passes - one for each
  rotation
• used to locate the triple points (junctions) on a
  skeleton.

10
Thinning

• NT(P1) no. of 0 to 1 transitions in the ordered
  sequence ,ltP2, P3, P9, P2gt
• NZ(P1) no. of non-zero neighbors of P1
• Set P1 to 0
• If 1ltNZ(P1)lt7 AND
• If NT(P1) 1 AND
• P2.P4.P8 0 OR NT(P2) .NE. 1 AND
• P2.P4.P6 0 OR NT(P4) .NE. 1

• Use both kernels and their 90o variations
• Consider all pixels on the boundaries of
  foreground regions. Delete pixel that has more
  than one foreground neighbor, as long as doing so
  does not locally disconnect
• Iterate until convergence.

11
Vornoi Diagrams and Convex Hulls
Thickening can be performed by thinning the
background Convex hull of a binary shape can be
visualized by imagining stretching an elastic
band around the shape. The elastic band will
follow the convex contours of the shape, but will
bridge' the concave contours.

• 1a and 1b are used for skeletonization of
  background.
• On each thickening iteration till convergence,
  each element is used in turn, and in each of its
  90 rotations.
• Structuring elements 2a and 2b are used similarly
  to prune the skeleton until convergence to get
  VORNOI diagram.

12
Connected Component Labeling

• Scan the image by moving along a row reach a
  point p to be labeled
• Examines neighbors of p which have already been
  encountered in the scan
• (i) to the left of p, (ii) above it, and (iii and
  iv) the two upper diagonal terms.
• If all four neighbors are 0, assign a new label
  to p
• else if only one neighbor is 1 assign its label
  to p
• else if one or more of the neighbors are 1 assign
  one of the labels to p and note the equivalences.
  
• After completing the scan, the equivalent label
  pairs are sorted into equivalence classes and a
  unique label is assigned to each class.

13
Binarization
14
Adaptive Thresholding
Adaptive (T mean) threshold with 7x7 neighborhood
Original gray scale
Global threshold
Adaptive (Tmean-C) threshold with 7x7
neighborhood C7 and C10
Using T median instead of the mean
15
Contour Tracing
16
Chain Code Contours
17
Features
Geometrical Features Sizes in x and y direction,
aspect ratio, perimeter, area Maximum and minimum
distances from boundary to center of
mass Compactness Perimeter2 / (4 Pi .
Area) Signatures projection profiles
Structural Features Number of holes Euler Number
no. of components no. of holes
Moments
area of the object
center of mass
18
X-Y Cuts
Autocorrelation function of the projection
profile, k is the lag parameter
If kkp is the first peak following the peak at
k0, sharpness of peak is given by
19
(No Transcript)
20
Smearing
Run Length Smearing (RLS) Change runs of white
pixels of length below a threshold to black
Vertical RLS and AND
Horizontal RLS
21
Fourier Transform

22
Document Images and FT
23
Hough Transform

• Parametric Form
• Global
• Peaks in Accumulator Space

24
(No Transcript)
25
Docstrum
Slope Histograms Use local information Connect a
mark (component) with K (4..6)
neighbors Histogram of the slopes More efficient
than projection profiles Docstrum is the radius
and angle plot of the slopes